Physics-Aware Recurrent and Graphical Learning for Robust Distribution System State Estimation Across Unseen Networks

Abstract

Robust distribution system state estimation (DSSE) is increasingly vital in modern, complex networks. However, conventional optimization-based DSSE approaches often face challenges with real-time complexity. Consequently, recent advancements have introduced learning-based, physics-aware DSSE methods that leverage network structural information to enhance model effectiveness. Among these methods, graph neural networks (GNNs) have gained prominence as an effective DSSE solution. However, GNN-based models encounter significant challenges in practical DSSE applications, due to their reliance on aggregating and propagating features only from adjacent nodes. To address this limitation, this paper presents a novel physics-aware DSSE framework. It integrates a structure-aware recurrent neural network and GNN with convolutional autoregressive moving average layers for improved feature aggregation and propagation. This model significantly enhances robustness and transferability across diverse unseen network topologies. The scalability of the proposed framework is demonstrated through extensive testing on real distribution systems. The proposed method demonstrates superior performance compared to existing methods by an average of 0.625% MAPE and 0.0093 rad. MAE in normal scenario under unseen networks. Additional ablation and measurement availability studies further verify the robustness and effectiveness of the proposed framework. The framework offers a pragmatic solution for DSSE, ensuring precise state estimation even in challenging environments.